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  • Acknowledgments. The authors wish to thank the Nepal Central Bureau of Statistics and the UN World Food Programme, without whose assistance this research would not have been possible. Opinions expressed in this article are the authors' own, and do not necessarily reflect those of either organization. The authors also acknowledge the substantial assistance of Dr Nicholas Longford at the earlier stages of this research, and thank the Associate Editor and two referees for their careful reading of the manuscript and their useful suggestions. Any errors, however, remain the responsibility of the authors only.

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Small-area estimation of poverty-related variables is an increasingly important analytical tool in targeting the delivery of food and other aid in developing countries. We compare two methods for the estimation of small-area means and proportions, namely empirical Bayes and composite estimation, with what has become the international standard method of Elbers, Lanjouw & Lanjouw (2003). In addition to differences among the sets of estimates and associated estimated standard errors, we discuss data requirements, design and model selection issues and computational complexity. The Elbers, Lanjouw and Lanjouw (ELL) method is found to produce broadly similar estimates but to have much smaller estimated standard errors than the other methods. The question of whether these standard error estimates are downwardly biased is discussed. Although the question cannot yet be answered in full, as a precautionary measure it is strongly recommended that the ELL model be modified to include a small-area-level error component in addition to the cluster-level and household-level errors it currently contains. This recommendation is particularly important because the allocation of billions of dollars of aid funding is being determined and monitored via ELL. Under current aid distribution mechanisms, any downward bias in estimates of standard error may lead to allocations that are suboptimal because distinctions are made between estimated poverty levels at the small-area level that are not significantly different statistically.